Overview

Dataset statistics

Number of variables11
Number of observations699
Missing cells0
Missing cells (%)0.0%
Duplicate rows8
Duplicate rows (%)1.1%
Total size in memory60.2 KiB
Average record size in memory88.2 B

Variable types

Numeric9
Categorical2

Alerts

Dataset has 8 (1.1%) duplicate rowsDuplicates
Clump Thickness is highly overall correlated with Uniformity of Cell Size and 6 other fieldsHigh correlation
Uniformity of Cell Size is highly overall correlated with Clump Thickness and 7 other fieldsHigh correlation
Uniformity of Cell Shape is highly overall correlated with Clump Thickness and 6 other fieldsHigh correlation
Marginal Adhesion is highly overall correlated with Clump Thickness and 6 other fieldsHigh correlation
Single Epithelial Cell Size is highly overall correlated with Clump Thickness and 6 other fieldsHigh correlation
Bland Chromatin is highly overall correlated with Clump Thickness and 6 other fieldsHigh correlation
Normal Nucleoli is highly overall correlated with Clump Thickness and 7 other fieldsHigh correlation
Mitoses is highly overall correlated with Uniformity of Cell Size and 2 other fieldsHigh correlation
Bare Nuclei is highly overall correlated with ClassHigh correlation
Class is highly overall correlated with Clump Thickness and 8 other fieldsHigh correlation

Reproduction

Analysis started2023-02-14 01:14:04.136694
Analysis finished2023-02-14 01:15:59.434174
Duration1 minute and 55.3 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Sample code number
Real number (ℝ)

Distinct645
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1071704.1
Minimum61634
Maximum13454352
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-02-13T19:16:00.400768image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum61634
5-th percentile411453
Q1870688.5
median1171710
Q31238298
95-th percentile1333890.8
Maximum13454352
Range13392718
Interquartile range (IQR)367609.5

Descriptive statistics

Standard deviation617095.73
Coefficient of variation (CV)0.57580794
Kurtosis257.71716
Mean1071704.1
Median Absolute Deviation (MAD)104381
Skewness13.675326
Sum7.4912116 × 108
Variance3.8080714 × 1011
MonotonicityNot monotonic
2023-02-13T19:16:02.290809image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1182404 6
 
0.9%
1276091 5
 
0.7%
1198641 3
 
0.4%
897471 2
 
0.3%
1116192 2
 
0.3%
385103 2
 
0.3%
411453 2
 
0.3%
1293439 2
 
0.3%
1143978 2
 
0.3%
560680 2
 
0.3%
Other values (635) 671
96.0%
ValueCountFrequency (%)
61634 1
0.1%
63375 1
0.1%
76389 1
0.1%
95719 1
0.1%
128059 1
0.1%
142932 1
0.1%
144888 1
0.1%
145447 1
0.1%
160296 1
0.1%
167528 1
0.1%
ValueCountFrequency (%)
13454352 1
0.1%
8233704 1
0.1%
1371920 1
0.1%
1371026 1
0.1%
1369821 1
0.1%
1368882 1
0.1%
1368273 1
0.1%
1368267 1
0.1%
1365328 1
0.1%
1365075 1
0.1%

Clump Thickness
Real number (ℝ)

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4177396
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-02-13T19:16:04.003132image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8157407
Coefficient of variation (CV)0.63737135
Kurtosis-0.62371541
Mean4.4177396
Median Absolute Deviation (MAD)2
Skewness0.59285853
Sum3088
Variance7.9283955
MonotonicityNot monotonic
2023-02-13T19:16:04.848434image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 145
20.7%
5 130
18.6%
3 108
15.5%
4 80
11.4%
10 69
9.9%
2 50
 
7.2%
8 46
 
6.6%
6 34
 
4.9%
7 23
 
3.3%
9 14
 
2.0%
ValueCountFrequency (%)
1 145
20.7%
2 50
 
7.2%
3 108
15.5%
4 80
11.4%
5 130
18.6%
6 34
 
4.9%
7 23
 
3.3%
8 46
 
6.6%
9 14
 
2.0%
10 69
9.9%
ValueCountFrequency (%)
10 69
9.9%
9 14
 
2.0%
8 46
 
6.6%
7 23
 
3.3%
6 34
 
4.9%
5 130
18.6%
4 80
11.4%
3 108
15.5%
2 50
 
7.2%
1 145
20.7%

Uniformity of Cell Size
Real number (ℝ)

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1344778
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-02-13T19:16:05.998560image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q35
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.0514591
Coefficient of variation (CV)0.97351434
Kurtosis0.098802885
Mean3.1344778
Median Absolute Deviation (MAD)0
Skewness1.2331366
Sum2191
Variance9.3114027
MonotonicityNot monotonic
2023-02-13T19:16:06.897808image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 384
54.9%
10 67
 
9.6%
3 52
 
7.4%
2 45
 
6.4%
4 40
 
5.7%
5 30
 
4.3%
8 29
 
4.1%
6 27
 
3.9%
7 19
 
2.7%
9 6
 
0.9%
ValueCountFrequency (%)
1 384
54.9%
2 45
 
6.4%
3 52
 
7.4%
4 40
 
5.7%
5 30
 
4.3%
6 27
 
3.9%
7 19
 
2.7%
8 29
 
4.1%
9 6
 
0.9%
10 67
 
9.6%
ValueCountFrequency (%)
10 67
 
9.6%
9 6
 
0.9%
8 29
 
4.1%
7 19
 
2.7%
6 27
 
3.9%
5 30
 
4.3%
4 40
 
5.7%
3 52
 
7.4%
2 45
 
6.4%
1 384
54.9%

Uniformity of Cell Shape
Real number (ℝ)

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2074392
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-02-13T19:16:07.978285image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q35
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9719128
Coefficient of variation (CV)0.9265687
Kurtosis0.00701098
Mean3.2074392
Median Absolute Deviation (MAD)0
Skewness1.1618592
Sum2242
Variance8.8322655
MonotonicityNot monotonic
2023-02-13T19:16:08.896372image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 353
50.5%
2 59
 
8.4%
10 58
 
8.3%
3 56
 
8.0%
4 44
 
6.3%
5 34
 
4.9%
6 30
 
4.3%
7 30
 
4.3%
8 28
 
4.0%
9 7
 
1.0%
ValueCountFrequency (%)
1 353
50.5%
2 59
 
8.4%
3 56
 
8.0%
4 44
 
6.3%
5 34
 
4.9%
6 30
 
4.3%
7 30
 
4.3%
8 28
 
4.0%
9 7
 
1.0%
10 58
 
8.3%
ValueCountFrequency (%)
10 58
 
8.3%
9 7
 
1.0%
8 28
 
4.0%
7 30
 
4.3%
6 30
 
4.3%
5 34
 
4.9%
4 44
 
6.3%
3 56
 
8.0%
2 59
 
8.4%
1 353
50.5%

Marginal Adhesion
Real number (ℝ)

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.806867
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-02-13T19:16:10.071551image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.8553792
Coefficient of variation (CV)1.0172834
Kurtosis0.98794707
Mean2.806867
Median Absolute Deviation (MAD)0
Skewness1.5244681
Sum1962
Variance8.1531906
MonotonicityNot monotonic
2023-02-13T19:16:11.195725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 407
58.2%
3 58
 
8.3%
2 58
 
8.3%
10 55
 
7.9%
4 33
 
4.7%
8 25
 
3.6%
5 23
 
3.3%
6 22
 
3.1%
7 13
 
1.9%
9 5
 
0.7%
ValueCountFrequency (%)
1 407
58.2%
2 58
 
8.3%
3 58
 
8.3%
4 33
 
4.7%
5 23
 
3.3%
6 22
 
3.1%
7 13
 
1.9%
8 25
 
3.6%
9 5
 
0.7%
10 55
 
7.9%
ValueCountFrequency (%)
10 55
 
7.9%
9 5
 
0.7%
8 25
 
3.6%
7 13
 
1.9%
6 22
 
3.1%
5 23
 
3.3%
4 33
 
4.7%
3 58
 
8.3%
2 58
 
8.3%
1 407
58.2%
Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2160229
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-02-13T19:16:12.241527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q34
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2142999
Coefficient of variation (CV)0.68852118
Kurtosis2.1690664
Mean3.2160229
Median Absolute Deviation (MAD)0
Skewness1.7121718
Sum2248
Variance4.903124
MonotonicityNot monotonic
2023-02-13T19:16:13.264512image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 386
55.2%
3 72
 
10.3%
4 48
 
6.9%
1 47
 
6.7%
6 41
 
5.9%
5 39
 
5.6%
10 31
 
4.4%
8 21
 
3.0%
7 12
 
1.7%
9 2
 
0.3%
ValueCountFrequency (%)
1 47
 
6.7%
2 386
55.2%
3 72
 
10.3%
4 48
 
6.9%
5 39
 
5.6%
6 41
 
5.9%
7 12
 
1.7%
8 21
 
3.0%
9 2
 
0.3%
10 31
 
4.4%
ValueCountFrequency (%)
10 31
 
4.4%
9 2
 
0.3%
8 21
 
3.0%
7 12
 
1.7%
6 41
 
5.9%
5 39
 
5.6%
4 48
 
6.9%
3 72
 
10.3%
2 386
55.2%
1 47
 
6.7%

Bare Nuclei
Categorical

Distinct11
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
1
402 
10
132 
2
 
30
5
 
30
3
 
28
Other values (6)
77 

Length

Max length2
Median length1
Mean length1.1888412
Min length1

Characters and Unicode

Total characters831
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row10
3rd row2
4th row4
5th row1

Common Values

ValueCountFrequency (%)
1 402
57.5%
10 132
 
18.9%
2 30
 
4.3%
5 30
 
4.3%
3 28
 
4.0%
8 21
 
3.0%
4 19
 
2.7%
? 16
 
2.3%
9 9
 
1.3%
7 8
 
1.1%

Length

2023-02-13T19:16:14.489272image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 402
57.5%
10 132
 
18.9%
2 30
 
4.3%
5 30
 
4.3%
3 28
 
4.0%
8 21
 
3.0%
4 19
 
2.7%
16
 
2.3%
9 9
 
1.3%
7 8
 
1.1%

Most occurring characters

ValueCountFrequency (%)
1 534
64.3%
0 132
 
15.9%
2 30
 
3.6%
5 30
 
3.6%
3 28
 
3.4%
8 21
 
2.5%
4 19
 
2.3%
? 16
 
1.9%
9 9
 
1.1%
7 8
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 815
98.1%
Other Punctuation 16
 
1.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 534
65.5%
0 132
 
16.2%
2 30
 
3.7%
5 30
 
3.7%
3 28
 
3.4%
8 21
 
2.6%
4 19
 
2.3%
9 9
 
1.1%
7 8
 
1.0%
6 4
 
0.5%
Other Punctuation
ValueCountFrequency (%)
? 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 831
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 534
64.3%
0 132
 
15.9%
2 30
 
3.6%
5 30
 
3.6%
3 28
 
3.4%
8 21
 
2.5%
4 19
 
2.3%
? 16
 
1.9%
9 9
 
1.1%
7 8
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 831
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 534
64.3%
0 132
 
15.9%
2 30
 
3.6%
5 30
 
3.6%
3 28
 
3.4%
8 21
 
2.5%
4 19
 
2.3%
? 16
 
1.9%
9 9
 
1.1%
7 8
 
1.0%

Bland Chromatin
Real number (ℝ)

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4377682
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-02-13T19:16:15.549588image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4383643
Coefficient of variation (CV)0.70928698
Kurtosis0.18462131
Mean3.4377682
Median Absolute Deviation (MAD)1
Skewness1.0999691
Sum2403
Variance5.9456202
MonotonicityNot monotonic
2023-02-13T19:16:16.602722image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 166
23.7%
3 165
23.6%
1 152
21.7%
7 73
10.4%
4 40
 
5.7%
5 34
 
4.9%
8 28
 
4.0%
10 20
 
2.9%
9 11
 
1.6%
6 10
 
1.4%
ValueCountFrequency (%)
1 152
21.7%
2 166
23.7%
3 165
23.6%
4 40
 
5.7%
5 34
 
4.9%
6 10
 
1.4%
7 73
10.4%
8 28
 
4.0%
9 11
 
1.6%
10 20
 
2.9%
ValueCountFrequency (%)
10 20
 
2.9%
9 11
 
1.6%
8 28
 
4.0%
7 73
10.4%
6 10
 
1.4%
5 34
 
4.9%
4 40
 
5.7%
3 165
23.6%
2 166
23.7%
1 152
21.7%

Normal Nucleoli
Real number (ℝ)

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8669528
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-02-13T19:16:17.862842image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.0536339
Coefficient of variation (CV)1.0651148
Kurtosis0.47426868
Mean2.8669528
Median Absolute Deviation (MAD)0
Skewness1.4222613
Sum2004
Variance9.32468
MonotonicityNot monotonic
2023-02-13T19:16:18.951617image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 443
63.4%
10 61
 
8.7%
3 44
 
6.3%
2 36
 
5.2%
8 24
 
3.4%
6 22
 
3.1%
5 19
 
2.7%
4 18
 
2.6%
7 16
 
2.3%
9 16
 
2.3%
ValueCountFrequency (%)
1 443
63.4%
2 36
 
5.2%
3 44
 
6.3%
4 18
 
2.6%
5 19
 
2.7%
6 22
 
3.1%
7 16
 
2.3%
8 24
 
3.4%
9 16
 
2.3%
10 61
 
8.7%
ValueCountFrequency (%)
10 61
 
8.7%
9 16
 
2.3%
8 24
 
3.4%
7 16
 
2.3%
6 22
 
3.1%
5 19
 
2.7%
4 18
 
2.6%
3 44
 
6.3%
2 36
 
5.2%
1 443
63.4%

Mitoses
Real number (ℝ)

Distinct9
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5894134
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-02-13T19:16:20.022210image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile5
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.7150779
Coefficient of variation (CV)1.0790634
Kurtosis12.657878
Mean1.5894134
Median Absolute Deviation (MAD)0
Skewness3.5606578
Sum1111
Variance2.9414923
MonotonicityNot monotonic
2023-02-13T19:16:21.033031image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 579
82.8%
2 35
 
5.0%
3 33
 
4.7%
10 14
 
2.0%
4 12
 
1.7%
7 9
 
1.3%
8 8
 
1.1%
5 6
 
0.9%
6 3
 
0.4%
ValueCountFrequency (%)
1 579
82.8%
2 35
 
5.0%
3 33
 
4.7%
4 12
 
1.7%
5 6
 
0.9%
6 3
 
0.4%
7 9
 
1.3%
8 8
 
1.1%
10 14
 
2.0%
ValueCountFrequency (%)
10 14
 
2.0%
8 8
 
1.1%
7 9
 
1.3%
6 3
 
0.4%
5 6
 
0.9%
4 12
 
1.7%
3 33
 
4.7%
2 35
 
5.0%
1 579
82.8%

Class
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2
458 
4
241 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters699
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 458
65.5%
4 241
34.5%

Length

2023-02-13T19:16:22.172941image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-13T19:16:23.508336image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2 458
65.5%
4 241
34.5%

Most occurring characters

ValueCountFrequency (%)
2 458
65.5%
4 241
34.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 699
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 458
65.5%
4 241
34.5%

Most occurring scripts

ValueCountFrequency (%)
Common 699
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 458
65.5%
4 241
34.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 699
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 458
65.5%
4 241
34.5%

Interactions

2023-02-13T19:15:45.298560image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:14:23.164201image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:14:35.016830image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:14:44.531558image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:14:54.163382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:04.015362image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:14.678014image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:24.902830image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:35.709626image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:46.618004image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:14:24.747594image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:14:36.226280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:14:45.718516image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:14:55.332581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:05.182060image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:16.060760image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:26.471818image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:36.751774image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:47.650431image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:14:26.530519image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:14:37.223366image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:14:46.723882image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:14:56.439291image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:06.247854image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:17.296153image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:27.465960image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:37.801531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:48.808547image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:14:27.746715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:14:38.147433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:14:48.014292image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:14:57.398463image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:08.336344image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:18.303246image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:28.814961image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:38.876500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:49.827981image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:14:28.820485image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:14:39.219220image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:14:49.070754image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:14:58.381557image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:09.548121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:19.438648image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:30.290131image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:40.110606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:50.772672image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:14:29.824605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:14:40.277128image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:14:49.869947image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:14:59.466619image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:10.607417image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:20.596714image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:31.236289image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:41.213423image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:51.781688image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:14:31.511759image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:14:41.453080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:14:51.010577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:00.527302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:11.667298image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:21.751559image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:32.556812image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:42.257631image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:52.789438image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:14:32.631289image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:14:42.480433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:14:52.032719image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:01.784632image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:12.578145image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:22.864093image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:33.675390image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:43.325751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:53.911920image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:14:33.983527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:14:43.461412image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:14:52.995878image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:02.968221image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:13.628788image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:23.951739image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:34.717294image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-13T19:15:44.402290image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-02-13T19:16:24.405439image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Sample code numberClump ThicknessUniformity of Cell SizeUniformity of Cell ShapeMarginal AdhesionSingle Epithelial Cell SizeBland ChromatinNormal NucleoliMitosesBare NucleiClass
Sample code number1.000-0.004-0.043-0.060-0.050-0.087-0.096-0.071-0.0750.0000.000
Clump Thickness-0.0041.0000.6660.6640.5420.5840.5380.5700.4190.2230.738
Uniformity of Cell Size-0.0430.6661.0000.8920.7430.7870.7190.7570.5090.2870.875
Uniformity of Cell Shape-0.0600.6640.8921.0000.7120.7590.6920.7250.4730.2780.860
Marginal Adhesion-0.0500.5420.7430.7121.0000.6680.6250.6340.4470.2630.738
Single Epithelial Cell Size-0.0870.5840.7870.7590.6681.0000.6400.7060.4800.2700.791
Bland Chromatin-0.0960.5380.7190.6920.6250.6401.0000.6620.3870.2550.804
Normal Nucleoli-0.0710.5700.7570.7250.6340.7060.6621.0000.5040.2510.768
Mitoses-0.0750.4190.5090.4730.4470.4800.3870.5041.0000.1940.519
Bare Nuclei0.0000.2230.2870.2780.2630.2700.2550.2510.1941.0000.834
Class0.0000.7380.8750.8600.7380.7910.8040.7680.5190.8341.000

Missing values

2023-02-13T19:15:56.457767image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-13T19:15:58.418555image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Sample code numberClump ThicknessUniformity of Cell SizeUniformity of Cell ShapeMarginal AdhesionSingle Epithelial Cell SizeBare NucleiBland ChromatinNormal NucleoliMitosesClass
010000255111213112
1100294554457103212
210154253111223112
310162776881343712
410170234113213112
510171228101087109714
6101809911112103112
710185612121213112
810330782111211152
910330784211212112
Sample code numberClump ThicknessUniformity of Cell SizeUniformity of Cell ShapeMarginal AdhesionSingle Epithelial Cell SizeBare NucleiBland ChromatinNormal NucleoliMitosesClass
6896545461111211182
6906545461113211112
691695091510105454414
6927140393111211112
6937632353111212122
6947767153111321112
6958417692111211112
6968888205101037381024
69789747148643410614
69889747148854510414

Duplicate rows

Most frequently occurring

Sample code numberClump ThicknessUniformity of Cell SizeUniformity of Cell ShapeMarginal AdhesionSingle Epithelial Cell SizeBare NucleiBland ChromatinNormal NucleoliMitosesClass# duplicates
0320675335231071142
146690611112111122
270409711111121122
3110052461010281073342
4111611691010110833142
5119864131112131122
6121886011111131122
7132194251112131122